A support vector machine-based context-ranking model for question answering

Yen, Show‐Jane; Wu, Yu-Chieh; Yang, Jie Chi; Lee, Yue‐Shi; Lee, Chung‐Jung; Liu, Jui-Jung

doi:10.1016/j.ins.2012.10.014

Cited by 63 publications

(28 citation statements)

References 33 publications

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“…Authors in [10] proposed head word features, which is one single word specifying the object that the question seeks. In [11], a framework has been proposed, which integrates a question classifier with a simple document/passage retriever, and proposed context-ranking models. In [12], a hybrid approach was proposed, named ATICM which is based on dependency tree analysis by utilizing both syntactic and semantic analysis.…”

Section: Question Classificationmentioning

confidence: 99%

Improving Imbalanced Question Classification Using Structured Smote Based Approach

Mohasseb

Bader-El-Den

Cocea

et al. 2018

2018 International Conference on Machine Learning and Cybernetics (ICMLC)

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Questions Classification (QC) is one of the most popular text classification applications. QC plays an important role in question-answering systems. However, as in many real-world classification problems, QC may suffer from the problem of class imbalance. The classification of imbalanced data has been a key problem in machine learning and data mining. In this paper, we propose a framework that deals with the class imbalance using a hierarchical SMOTE algorithm for balancing different types of questions. The proposed framework is grammar-based, which involves using the grammatical pattern for each question and using machine learning algorithms to classify them. Experimental results imply that the proposed framework demonstrates a good level of accuracy in identifying different question types and handling class imbalance.

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Section: Question Classificationmentioning

confidence: 99%

Improving Imbalanced Question Classification Using Structured Smote Based Approach

Mohasseb

Bader-El-Den

Cocea

et al. 2018

2018 International Conference on Machine Learning and Cybernetics (ICMLC)

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“…knowledge exploitation or knowledge application) can be divided in two subgroups: knowledge utilization and knowledge transfer. At the same time, the utilization of knowledge can be used for knowledge reasoning or for knowledge retrieval (in the way the Question and Answering (Q & A) systems work [44]). Meanwhile, the purpose of knowledge sharing (a.k.a.…”

Section: State-of-the-artmentioning

confidence: 99%

Automated knowledge base management: A survey

Martinez‐Gil¹

2017

Preprint

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A fundamental challenge in the intersection of Artificial Intelligence and Databases consists of developing methods to automatically manage Knowledge Bases which can serve as a knowledge source for computer systems trying to replicate the decision-making ability of human experts. Despite of most of tasks involved in the building, exploitation and maintenance of KBs are far from being trivial, significant progress has been made during the last years. However, there are still a number of challenges that remain open. In fact, there are some issues to be addressed in order to empirically prove the technology for systems of this kind to be mature and reliable.

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“…Recent studies classified users' questions using different features like bag-of-words [54], [24], [53], [31], semantic and syntactic features [53], [13], [49], and uni-gram and word shape features [17]. Authors in [17] stated that features are the key to obtain an accurate question classifier.…”

Section: Introductionmentioning

confidence: 99%

Question categorization and classification using grammar based approach

Mohasseb

Bader-El-Den

Cocea

2018

Information Processing & Management

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Question-answering has become one of the most popular information retrieval applications. Despite that most questionanswering systems try to improve the user experience and the technology used in finding relevant results, many difficulties are still faced because of the continuous increase in the amount of web content. Questions Classification (QC) plays an important role in question-answering systems, with one of the major tasks in the enhancement of the classification process being the identification of questions types. A broad range of QC approaches has been proposed with the aim of helping to find a solution for the classification problems; most of these are approaches based on bag-of-words or dictionaries. In this research, we present an analysis of the different type of questions based on their grammatical structure. We identify different patterns and use machine learning algorithms to classify them. A framework is proposed for question classification using a grammar-based approach (GQCC) which exploits the structure of the questions. Our findings indicate that using syntactic categories related to different domain-specific types of Common Nouns, Numeral Numbers and Proper Nouns enable the machine learning algorithms to better differentiate between different question types. The paper presents a wide range of experiments the results show that the GQCC using J48 classifier has outperformed other classification methods with 90.1% accuracy.

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A support vector machine-based context-ranking model for question answering

Cited by 63 publications

References 33 publications

Improving Imbalanced Question Classification Using Structured Smote Based Approach

Improving Imbalanced Question Classification Using Structured Smote Based Approach

Automated knowledge base management: A survey

Question categorization and classification using grammar based approach

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